SOTAVerified

Data Augmentation

Data augmentation involves techniques used for increasing the amount of data, based on different modifications, to expand the amount of examples in the original dataset. Data augmentation not only helps to grow the dataset but it also increases the diversity of the dataset. When training machine learning models, data augmentation acts as a regularizer and helps to avoid overfitting.

Data augmentation techniques have been found useful in domains like NLP and computer vision. In computer vision, transformations like cropping, flipping, and rotation are used. In NLP, data augmentation techniques can include swapping, deletion, random insertion, among others.

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Papers

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Showing 6751–6800 of 8378 papers

TitleStatusHype
Detecting Methane Plumes using PRISMA: Deep Learning Model and Data Augmentation—0
Detecting Mitoses with a Convolutional Neural Network for MIDOG 2022 Challenge—0
Detecting Mitosis against Domain Shift using a Fused Detector and Deep Ensemble Classification Model for MIDOG Challenge—0
Detecting Prefix Bias in LLM-based Reward Models—0
Detection and Classification of Brain tumors Using Deep Convolutional Neural Networks—0
Detection of Active Emergency Vehicles using Per-Frame CNNs and Output Smoothing—0
Detection of Lexical Stress Errors in Non-Native (L2) English with Data Augmentation and Attention—0
Detection of pulmonary pathologies using convolutional neural networks, Data Augmentation, ResNet50 and Vision Transformers—0
Detection of Suicidal Risk on Social Media: A Hybrid Model—0
Detection of Synthetic Face Images: Accuracy, Robustness, Generalization—0
Detection Transformer for Teeth Detection, Segmentation, and Numbering in Oral Rare Diseases: Focus on Data Augmentation and Inpainting Techniques—0
Deterministic Certification to Adversarial Attacks via Bernstein Polynomial Approximation—0
Developing a Component Comment Extractor from Product Reviews on E-Commerce Sites—0
Developing efficient transfer learning strategies for robust scene recognition in mobile robotics using pre-trained convolutional neural networks—0
Developing neural machine translation models for Hungarian-English—0
Developing the Reliable Shallow Supervised Learning for Thermal Comfort using ASHRAE RP-884 and ASHRAE Global Thermal Comfort Database II—0
DFlow: Diverse Dialogue Flow Simulation with Large Language Models—0
DG2: Data Augmentation Through Document Grounded Dialogue Generation—0
DG2: Data Augmentation Through Document Grounded Dialogue Generation—0
DHA: End-to-End Joint Optimization of Data Augmentation Policy, Hyper-parameter and Architecture—0
Diabetes detection using deep learning techniques with oversampling and feature augmentation—0
Diabetic Retinopathy Detection Using CNN with Residual Block with DCGAN—0
Diabetic retinopathy image classification method based on GreenBen data augmentation—0
Diagnosing Bipolar Disorder from 3-D Structural Magnetic Resonance Images Using a Hybrid GAN-CNN Method—0
Diagnosis, Feedback, Adaptation: A Human-in-the-Loop Framework for Test-Time Policy Adaptation—0
Diagonal Symmetrization of Neural Network Solvers for the Many-Electron Schrödinger Equation—0
DialAug: Mixing up Dialogue Contexts in Contrastive Learning for Robust Conversational Modeling—0
Dialect Adaptation and Data Augmentation for Low-Resource ASR: TalTech Systems for the MADASR 2023 Challenge—0
DialoGPS: Dialogue Path Sampling in Continuous Semantic Space for Data Augmentation in Multi-Turn Conversations—0
DiCOVA-Net: Diagnosing COVID-19 using Acoustics based on Deep Residual Network for the DiCOVA Challenge 2021—0
Dictionary-based Data Augmentation for Cross-Domain Neural Machine Translation—0
DifAugGAN: A Practical Diffusion-style Data Augmentation for GAN-based Single Image Super-resolution—0
Diff-2-in-1: Bridging Generation and Dense Perception with Diffusion Models—0
Cap2Aug: Caption guided Image to Image data Augmentation—0
DiffAutoML: Differentiable Joint Optimization for Efficient End-to-End Automated Machine Learning—0
DiffECG: A Versatile Probabilistic Diffusion Model for ECG Signals Synthesis—0
Differential Diagnosis of Frontotemporal Dementia and Alzheimer's Disease using Generative Adversarial Network—0
Differential Expression Analysis of Dynamical Sequencing Count Data with a Gamma Markov Chain—0
diffIRM: A Diffusion-Augmented Invariant Risk Minimization Framework for Spatiotemporal Prediction over Graphs—0
Diff-Lung: Diffusion-Based Texture Synthesis for Enhanced Pathological Tissue Segmentation in Lung CT Scans—0
DiffPop: Plausibility-Guided Object Placement Diffusion for Image Composition—0
DiffStitch: Boosting Offline Reinforcement Learning with Diffusion-based Trajectory Stitching—0
DiffuseMix: Label-Preserving Data Augmentation with Diffusion Models—0
Diffusion-augmented Graph Contrastive Learning for Collaborative Filter—0
Diffusion-based Data Augmentation for Object Counting Problems—0
Diffusion-based Data Augmentation for Skin Disease Classification: Impact Across Original Medical Datasets to Fully Synthetic Images—0
Diffusion Bridge Models for 3D Medical Image Translation—0
DiffusionEngine: Diffusion Model is Scalable Data Engine for Object Detection—0
Diffusion Model-based Data Augmentation Method for Fetal Head Ultrasound Segmentation—0
Diffusion Models for Robotic Manipulation: A Survey—0
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All datasetsImageNetCIFAR-10GA1457

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1DeiT-B (+MixPro)Accuracy (%)82.9—Unverified
2ResNet-200 (DeepAA)Accuracy (%)81.32—Unverified
3DeiT-S (+MixPro)Accuracy (%)81.3—Unverified
4ResNet-200 (Fast AA)Accuracy (%)80.6—Unverified
5ResNet-200 (UA)Accuracy (%)80.4—Unverified
6ResNet-200 (AA)Accuracy (%)80—Unverified
7ResNet-50 (DeepAA)Accuracy (%)78.3—Unverified
8ResNet-50 (TA wide)Accuracy (%)78.07—Unverified
9ResNet-50 (LoRot-E)Accuracy (%)77.72—Unverified
10ResNet-50 (LoRot-I)Accuracy (%)77.71—Unverified
#ModelMetricClaimedVerifiedStatus
1WideResNet-40-2 (Faster AA)Percentage error3.7—Unverified
2Shake-Shake (26 2×32d) (Faster AA)Percentage error2.7—Unverified
3WideResNet-28-10 (Faster AA)Percentage error2.6—Unverified
4Shake-Shake (26 2×96d) (Faster AA)Percentage error2—Unverified
5Shake-Shake (26 2×112d) (Faster AA)Percentage error2—Unverified
#ModelMetricClaimedVerifiedStatus
1DiffAugClassification Accuracy92.7—Unverified
2PaCMAPClassification Accuracy85.3—Unverified
3hNNEClassification Accuracy77.4—Unverified
4TopoAEClassification Accuracy74.6—Unverified